def getShiftFromFFTs(fft1, fft2, v1, v2, minOverlap, nHighestPeaks):
pcm = PhaseCorrelation2.calculatePCMInPlace(fft1, fft2,
ArrayImgFactory(FloatType()),
FloatType(), exe)
peak = PhaseCorrelation2.getShift(pcm, v1, v2, nHighestPeaks, minOverlap,
True, True, exe)
spshift = peak.getSubpixelShift()
if spshift is not None:
return spshift.getFloatPosition(0), spshift.getFloatPosition(1)
else:
IJ.log('There is a peak.getSubpixelShift issue. sFOV ' + str(sFOV) +
' s ' + str(s))
return None
def CreatePhantom(self):
if (self.numChannels > 1):
image = self.ops.run("create", [
self.objectSizeX, self.objectSizeY, self.objectSizeZ,
self.numChannels
], FloatType())
else:
image = self.ops.run(
"create",
[self.objectSizeX, self.objectSizeY, self.objectSizeZ],
FloatType())
ax = [Axes.X, Axes.Y, Axes.Z, Axes.CHANNEL]
self.phantom = ImgPlus(image, "phantom", ax)
def getShiftFromViews(v1, v2):
# Thread pool
exe = Executors.newFixedThreadPool(
Runtime.getRuntime().availableProcessors())
try:
# PCM: phase correlation matrix
pcm = PhaseCorrelation2.calculatePCM(
v1, v2, ArrayImgFactory(FloatType()), FloatType(),
ArrayImgFactory(ComplexFloatType()), ComplexFloatType(), exe)
# Minimum image overlap to consider, in pixels
minOverlap = v1.dimension(0) / 10
# Returns an instance of PhaseCorrelationPeak2
peak = PhaseCorrelation2.getShift(pcm, v1, v2, nHighestPeaks,
minOverlap, True, True, exe)
except Exception, e:
print e
def MakeMultiChannelPhantom(ops, size):
if len(size) > 3:
numChannels = size[3]
else:
numChannels = 1
image = ops.run("create", size, FloatType())
ax = [Axes.X, Axes.Y, Axes.Z, Axes.CHANNEL]
imgPlus = ImgPlus(image, "phantom", ax)
location = zeros(3, 'i')
location[0] = 40
location[1] = size[1] / 2
location[2] = size[2] / 2
#ops.run("addsphere", image, location, radius, 1.0)
#ops.run("addassymetricspherel", image, location, 1.0, radius1, radius2)
shapes = Add3DShapes(ops, size)
def AddShapes(hyperSlice):
#shapes.addRandomPointsInROI(hyperSlice, 100.0, 20)
shapes.addCenterSphere(hyperSlice, 5.0, 20)
if (numChannels > 1):
for d in range(0, numChannels):
hyperSlice = Views.hyperSlice(image, 3, d)
AddShapes(hyperSlice)
location[0] += 10
else:
AddShapes(image)
return imgPlus
def combine(op, title, *ops): edges_img = img.factory().imgFactory(FloatType()).create(img) compute(op(*ops)).into(edges_img) imp = IL.wrap(edges_img, title) imp.getProcessor().resetMinAndMax() imp.show() return imp
def createType(bytesPerPixel):
if 1:
return UnsignedByteType()
if 2:
return UnsignedShortType()
if 4:
return FloatType()
if 8:
return UnsignedLongType()
def SpotDetectionGray(gray, data, display, ops, invert):
# get the dimensions
dimensions2D = array([gray.dimension(0), gray.dimension(1)], 'l')
factory = gray.getImg().factory()
# wrap as ImagePlus
imp = ImageJFunctions.wrap(gray, "wrapped")
# create and call background subtractor
bgs = BackgroundSubtracter()
bgs.rollingBallBackground(imp.getProcessor(), 50.0, False, False, True,
True, True)
# wrap the result of background subtraction as Img and display it
iplus = ImagePlus("bgs", imp.getProcessor())
# if (invert==True):
# iplus.getProcessor().invert()
imgBgs = ImageJFunctions.wrapByte(iplus)
display.createDisplay("back_sub", data.create(ImgPlus(imgBgs)))
# convert the background subtracted image to 32 bit
temp = ops.run("createimg", factory, FloatType(), dimensions2D)
imgBgs32 = ImgPlus(temp)
ops.convert(imgBgs32, imgBgs, ConvertPixCopy())
#display.createDisplay("back_sub 32", data.create(ImgPlus(imgBgs32)))
# create the Laplacian of Gaussian filter
kernel = DetectionUtils.createLoGKernel(3.0, 2, array([1.0, 1.0], 'd'))
# apply the log filter and display the result
log = ImgPlus(ops.run("createimg", factory, FloatType(), dimensions2D))
ops.convolve(log, imgBgs32, kernel)
#display.createDisplay("log", data.create(ImgPlus(log)))
# apply the threshold operation
#thresholded=ops.run("threshold", ops.create( dimensions2D, BitType()), log, Triangle())
thresholded = ops.run("triangle", log)
return ImgPlus(thresholded)
def SpotDetectionLog(img, data, ops, thresholdmethod, dimensions2D, factory):
# create the Laplacian of Gaussian filter
kernel = DetectionUtils.createLoGKernel( 3.0, 2, array([1.0, 1.0], 'd' ) )
# apply the log filter and display the result
log=ImgPlus( ops.run("createimg", factory, FloatType(), dimensions2D) )
ops.convolve(log, img, kernel)
# apply the threshold operation
thresholded = ops.run(thresholdmethod, log)
class FnCursor(Point, Cursor, RandomAccess):
def __init__(self, n, wavelength, offset):
super(Point, self).__init__(n)
self.offset = offset # in radians
self.wavelength = wavelength
self.t = FloatType()
def copyRandomAccess(self):
return FnCursor(self.numDimensions(), self.wavelength, self.offset)
def copyCursor(self):
return self.copyRandomAccess()
def get(self):
x = self.getLongPosition(0)
y = self.getLongPosition(1)
val = sin(x * 2 * pi / self.wavelength + self.offset)\
+ sin(y * 2 * pi / self.wavelength + self.offset)
self.t.set(val)
if 0 == x % 64 and 0 == y % 64:
System.out.println("x, y: " + str(x) + ", " + str(y) + " :: " +
str(val))
return t
# spot detection routine for gray scale data. Detects light objects
def SpotDetectionGray(gray, data, display, ops, thresholdmethod, showSteps=False):
# get the dimensions
dimensions2D=array( [gray.dimension(0), gray.dimension(1)], 'l')
factory=gray.getImg().factory()
if (invert==True):
imp.getProcessor().invert()
imgBgs=gray;#ImageJFunctions.wrapByte(imp)
if (showSteps): display.createDisplay("back_sub", data.create(ImgPlus(imgBgs)))
# convert the background subtracted image to 32 bit
temp=ops.run( "createimg", factory, FloatType(), dimensions2D )
imgBgs32=ImgPlus( temp )
ops.convert(imgBgs32, imgBgs, ConvertPixCopy() )
#display.createDisplay("back_sub 32", data.create(ImgPlus(imgBgs32)))
def filterBankRotations(img,
angles=xrange(0, 46, 9), # sequence, in degrees
filterBankFn=filterBank, # function that takes an img as sole positional argument
outputType=FloatType()):
""" img: a RandomAccessibleInterval.
filterBankFn: the function from which to obtain a sequence of ImgMath ops.
angles: a sequence of angles in degrees.
outputType: for materializing rotated operations and rotating them back.
For every angle, will prepare a rotated view of the image,
then create a list of ops on the basis of that rotated view,
then materialize each op into an image so that an unrotated view
can be returned back.
returns a list of unrotated views, each containing the values of applying
each op to the rotated view.
"""
ops_rotations = []
for angle in angles:
imgRot = img if 0 == angle else rotatedView(img, angle)
ops = filterBankFn(imgRot)
# Materialize these two combination ops and rotate them back (rather, a rotated view)
interval = Intervals.translate(img, [(imgRot.dimension(d) - img.dimension(d)) / 2
for d in xrange(img.numDimensions())])
for op in ops:
imgOpRot = compute(op).intoArrayImg(outputType)
if 0 == angle:
ops_rotations.append(imgOpRot)
continue
# Rotate them back and crop view
imgOpUnrot = rotatedView(imgOpRot, -angle, enlarge=False)
imgOp = Views.zeroMin(Views.interval(imgOpUnrot, interval))
#if angle == 0 or angle == 45:
# IL.wrap(imgOpRot, "imgOpRot angle=%i" % angle).show()
# IL.wrap(imgOpUnrot, "imgOpUnrot angle=%i" % angle).show()
# IL.wrap(imgOp, "imgOp angle=%i" % angle).show()
ops_rotations.append(imgOp)
return ops_rotations
c.fwd()
c.localize(pos)
print "Cursor:", pos, "::", c.get()
# Test RandomAccess
ra = iraf.randomAccess()
c = iraf.cursor()
while c.hasNext():
c.fwd()
ra.setPosition(c)
c.localize(pos)
print "RandomAccess:", pos, "::", ra.get()
# Test source img: should be untouched
c = img.cursor()
while c.hasNext():
print "source:", c.next()
# Test interval view: the middle 2x2 square
v = Views.interval(iraf, [1, 1], [2, 2])
IL.wrap(v, "+2 view").show()
# An array from 0 to 15
a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
pixels = array(a, 'b')
img = ArrayImgs.unsignedBytes(pixels, [4, 4])
test(add(img, 2).view())
test(add(img, 2).view(FloatType()))
# MIP Red
red_mip = getMIP(red)
if debugging:
ui.show('RED_MIP',red_mip)
# Calculate T = Mean+Std*.25
# Set threshold (T,65k)
# Convert to Mask
Rmean = ops.stats().mean(red_mip).getRealDouble()
Rstd = ops.stats().stdDev(red_mip).getRealDouble()
T = Rmean*.25
# D for particlemask
D = Rmean + Rstd*0.25
log('Calculated threshold for the red is {}'.format(T))
red_mask = ops.threshold().apply(ops.convert().float32(red_mip),FloatType(T))
particlemask = ops.threshold().apply(ops.convert().float32(red_mip),FloatType(D))
# "Opening" with "Area"=5 px^2
# "Dilation" with Neighb=1, Count=1
red_mask = ops.morphology().open(red_mask,[HyperSphereShape(2)])
red_mask = ops.morphology().dilate(red_mask,HyperSphereShape(1))
particlemask = ops.morphology().open(particlemask,[HyperSphereShape(2)])
particlemask = ops.morphology().dilate(particlemask,HyperSphereShape(1))
# Perform Top Hat with area=maxPSize
r = math.ceil(math.sqrt(maxPSize/3.141593))
green_tophat = ops.morphology().topHat(green_mip,[HyperSphereShape(long(r))])
if debugging:
def __init__(self, n, wavelength, offset):
super(Point, self).__init__(n)
self.offset = offset # in radians
self.wavelength = wavelength
self.t = FloatType()
from net.imglib2.type.numeric.real import FloatType ft = FloatType(10) ft.pow(2) print ft from net.imglib2.algorithm.math.ImgMath import compute, add, power from net.imglib2.img.array import ArrayImgs from net.imglib2.img.display.imagej import ImageJFunctions as IL img = ArrayImgs.floats([10, 10, 10]) compute(add(img, 5)).into(img) # in place compute(power(img, 2)).into(img) # in place print 25 * 10 * 10 * 10 == sum(t.get() for t in img.cursor()) IL.wrap(img, "5 squared").show()
self.offset = offset # in radians
self.wavelength = wavelength
def getValue(self, index):
x = index % self.width
y = index / self.width
val = sin(x * 2 * pi / self.wavelength + self.offset)\
+ sin(y * 2 * pi / self.wavelength + self.offset)
return min(int(val * 65535 / 2), 65535)
def putValue(self, index, value):
pass # ignore
sinaccess = SinDataAccess(dimensions[0], -pi / 2, dimensions[0] / 4)
t = FloatType()
img = ArrayImg(sinaccess, dimensions, t.getEntitiesPerPixel())
img.setLinkedType(t.getNativeTypeFactory().createLinkedType(img))
#IL.show(img)
# 5. Functional image but without faking an ArrayImg
from net.imglib2.img import AbstractImg
from net.imglib2 import Cursor, RandomAccess, Point
from net.imglib2.type.numeric.real import FloatType
from math import sin, pi
from java.lang import System
class FnCursor(Point, Cursor, RandomAccess):
def __init__(self, n, wavelength, offset):
int_converter = Util.genericIntegerTypeConverter()
# Create the integral image of an 8-bit input, stored as 64-bit
alg = IntegralImg(img, UnsignedLongType(), int_converter)
alg.process()
integralImg = alg.getResult()
# Read out blocks of radius 5 (i.e. 10x10 for a 2d image)
# in a way that is entirely n-dimensional (applies to 1d, 2d, 3d, 4d ...)
radius = 5
nd = img.numDimensions()
op = div(block(Views.extendBorder(integralImg), [radius] * nd),
pow(radius * 2, nd))
blurred = img.factory().create(img) # an 8-bit image
# Compute in floats, store result into longs
# using a default generic RealType converter via t.setReal(t.getRealDouble())
compute(op).into(blurred, None, FloatType(), None)
# Show the blurred image with the same LUT as the original
imp2 = IL.wrap(blurred, "integral image radius 5 blur")
imp2.getProcessor().setLut(imp.getProcessor().getLut())
imp2.show()
# Compare with Gaussian blur
from ij import ImagePlus
from ij.plugin.filter import GaussianBlur
from ij.gui import Line, ProfilePlot
# Gaussian of the original image
imp_gauss = ImagePlus(imp.getTitle() + " Gauss",
imp.getProcessor().duplicate())
GaussianBlur().blurGaussian(imp_gauss.getProcessor(), radius)
# @OpService ops # @Dataset data from net.imglib2.type.numeric.real import FloatType print ops.stats().sum(FloatType(), data)
#create the phantom
phantom = spheres.CreatePhantom()
# create the phantom
#phantom=MakeMultiChannelPhantom(ops, size)
#MakeMultiChannelPhantom([256, 256, 128, 5])
display.createDisplay("phantom", data.create(phantom));
# create the psf
psf = ops.run("psf", psfsize[0], psfsize[2], [100, 100, 300], 400, 1.4, 1.51, 1.51, 10)
display.createDisplay("psf", data.create(psf));
#extended = ops.run("extend", None, phantom, extensionxy, extensionz, BoundaryType.ZERO, FFTTarget.MINES_SPEED)
extended = ops.run("extend", None, phantom, spheres.objectSizeX, spheres.objectSizeY, spheres.objectSizeZ, ExtensionType.DIMENSION, BoundaryType.ZERO, FFTTarget.MINES_SPEED)
display.createDisplay("extended", data.create(extended));
background=FloatType()
background.setReal(0.0001)
ops.run("add", extended, background)
extendedPsf = ops.run("extend", None, psf, spheres.objectSizeX, spheres.objectSizeY, spheres.objectSizeZ, ExtensionType.DIMENSION, BoundaryType.ZERO, FFTTarget.MINES_SPEED)
#extendedPsf = ops.run("extend", None, psf, extensionxy, 0, BoundaryType.ZERO, FFTTarget.MINES_SPEED)
display.createDisplay("extended psf", data.create(extendedPsf));
# convolve
convolved=ops.run("frequencyfilter", extended, extendedPsf, ConvolutionRaiRai())
display.createDisplay("convolved", data.create(convolved))
extendedDimensions=[]
for i in range(0,3):
extendedDimensions.append(extended.dimension(i))
directory="/home/bnorthan/Brian2014/Projects/deconware2/deconware-scripts/images/"
helaCellsName="helacellsredcropped.tif"
barsName="Bars.tif"
helaCells=data.open(directory+helaCellsName);
bars=data.open(directory+barsName);
display.createDisplay("HelaCells", helaCells);
gaussianKernel =ops.gaussKernel( 2, 5.0);
gaussianKernel3D =ops.gaussKernel(array([4.0, 4.0, 2.0], 'd'));
#logKernel=ops.logKernel(2, 3.0, None, FloatType(),PlanarImgFactory())
logKernel=ops.logKernel(2, 3.0)
display.createDisplay("gausskernel", ImgPlus(gaussianKernel));
dimensions2D=array([helaCells.dimension(0), helaCells.dimension(1)], 'l');
dimensions3D=array([bars.dimension(0), bars.dimension(1), bars.dimension(2)], 'l');
gaussianFiltered=ops.createImg( dimensions2D)
logFiltered=ops.createImg(FloatType(), PlanarImgFactory(), dimensions2D)
barsGaussianFiltered=ops.createImg(FloatType(), PlanarImgFactory(), dimensions3D)
ops.convolve(gaussianFiltered, helaCells, gaussianKernel)
ops.convolve(logFiltered, helaCells, logKernel)
ops.convolve(barsGaussianFiltered, bars, gaussianKernel3D);
display.createDisplay("gaussian", ImgPlus(gaussianFiltered));
display.createDisplay("log", ImgPlus(logFiltered));
display.createDisplay("bars convolved", ImgPlus(barsGaussianFiltered));
# Load an RGB or ARGB image
#imp = IJ.openImage("https://imagej.nih.gov/ij/images/leaf.jpg")
imp = IJ.getImage()
# Access its pixel data from an ImgLib2 data structure:
# a RandomAccessibleInterval<ARGBType>
img = IL.wrapRGBA(imp)
# Read out single channels
red = Converters.argbChannel(img, 1)
green = Converters.argbChannel(img, 2)
blue = Converters.argbChannel(img, 3)
# Create an empty image of type FloatType (floating-point values)
# Here, the img is used to read out the interval: the dimensions for the new image
brightness = ArrayImgFactory(FloatType()).create(img)
# Compute the brightness: pick the maximum intensity pixel of every channel
# and then normalize it by dividing by the number of channels
compute(div(maximum([red, green, blue]), 255.0)).into(brightness)
# Show the brightness image
impB = IL.wrap(brightness, imp.getTitle() + " brightness")
impB.show()
# Compute now the image color saturation
saturation = ArrayImgFactory(FloatType()).create(img)
compute(
let(
"red",
red, # store as directly readable variables (no dictionary lookups)
# Scale the X,Y axis down to isotropy with the Z axis
cal = imp.getCalibration()
scale2D = cal.pixelWidth / cal.pixelDepth
iso = Compute.inFloats(Scale2D(Red(ImgLib.wrap(imp)), scale2D))
#ImgLib.wrap(iso).show()
# Find peaks by difference of Gaussian
sigma = (cell_diameter / cal.pixelWidth) * scale2D
peaks = DoGPeaks(iso, sigma, sigma * 0.5, minPeak, 1)
print "Found", len(peaks), "peaks"
# Copy ImgLib1 iso image into ImgLib2 copy image
copy = ArrayImgFactory().create(
[iso.getDimension(0),
iso.getDimension(1),
iso.getDimension(2)], FloatType())
c1 = iso.createCursor()
c2 = copy.cursor()
while c1.hasNext():
c1.fwd()
c2.fwd()
c2.get().set(c1.getType().getRealFloat())
#ImageJFunctions.show(copy)
# Measure mean intensity at every peak
sphereFactory = HyperSphereNeighborhood.factory()
radius = 2
intensities1 = []
copy2 = Views.extendValue(copy, FloatType(0))
ra = copy2.randomAccess()
aff.set(*matrix)
return aff
# Transform the kernel for each view
kernels = [kernel,
transformPSFKernelToView(kernel, affine3D(matrices["imgB0-imgB1"])),
transformPSFKernelToView(kernel, affine3D(matrices["imgB0-imgB2"])),
transformPSFKernelToView(kernel, affine3D(matrices["imgB0-imgB3"]))]
def deconvolve(images, kernels, name, n_iterations):
# Bayesian-based multi-view deconvolution
exe = newFixedThreadPool(Runtime.getRuntime().availableProcessors() -2)
try:
mylambda = 0.0006
blockSize = Intervals.dimensionsAsIntArray(images[0]) # [128, 128, 128]
cptf = ComputeBlockSeqThreadCPUFactory(exe, mylambda, blockSize, ArrayImgFactory(FloatType()))
psiInitFactory = PsiInitBlurredFusedFactory() # PsiInitAvgPreciseFactory() fails with type mismatch: UnsignedByteType (?) vs FloatType
weight = Views.interval(ConstantRandomAccessible(FloatType(1), images[0].numDimensions()), FinalInterval(images[0]))
filterBlocksForContent = False # Run once with True, none were removed
decon_views = DeconViews([DeconView(exe, img, weight, kernel, PSFTYPE.INDEPENDENT, blockSize, 1, filterBlocksForContent)
for img in images],
exe)
#n_iterations = 10
decon = MultiViewDeconvolutionSeq(decon_views, n_iterations, psiInitFactory, cptf, ArrayImgFactory(FloatType()))
if not decon.initWasSuccessful():
print "Something went wrong initializing MultiViewDeconvolution"
else:
decon.runIterations()
img = decon.getPSI()
imp = IL.wrap(img, name + "_deconvolved_" + str(n_iterations) + "_iterations")
imp.show()
# create and call background subtractor
bgs = BackgroundSubtracter()
bgs.rollingBallBackground(imp.getProcessor(), 50.0, False, False, True, True,
True)
# wrap the result of background subtraction as Img
iplus = ImagePlus("bgs", imp.getProcessor())
imgBgs = ImageJFunctions.wrapShort(iplus)
###############################################################
# Step 2: Laplacian of Gaussian Filtering
###############################################################
# convert to 32 bit
imgBgs32 = ops.run("createimg", imgBgs, FloatType())
ops.convert(imgBgs32, imgBgs, ConvertPixCopy())
# create the Laplacian of Gaussian filter
kernel = DetectionUtils.createLoGKernel(3.0, 2, array([1.0, 1.0], 'd'))
# create the output Img for convolution
log = ImgPlus(ops.run("createimg", inputData.getImgPlus(), FloatType()))
# apply the log filter
ops.convolve(log, imgBgs32, kernel)
###############################################################
# Step 3: Threshold
###############################################################
# @DatasetService data
# @DisplayService display
# @IOService io
# @OpService ops
# @net.imagej.Dataset image
# @net.imagej.Dataset psf
from net.imglib2.meta import ImgPlus
from net.imglib2.type.numeric.real import FloatType
from jarray import array
sumImg=ops.sum(FloatType(), image.getImgPlus())
sumPsf=ops.sum(FloatType(), psf.getImgPlus())
convolved=ops.convolve(image.getImgPlus(), psf.getImgPlus());
display.createDisplay("convolved", ImgPlus(convolved));
size=array([0, 0, 0], 'l')
convolved2=ops.convolve(None, image, psf, size)
display.createDisplay("convolved2", ImgPlus(convolved2));
sumConvolved=ops.sum(FloatType(), convolved)
sumConvolved2=ops.sum(FloatType(), convolved2)
print sumImg.getRealDouble()
print sumPsf.getRealDouble()
print sumImg.getRealDouble()*sumPsf.getRealDouble()
print sumConvolved.getRealDouble()
print sumConvolved2.getRealDouble()
psfX / 2 + psfXSize / 2 - 1,
psfY / 2 + psfYSize / 2 - 1, psfZ - 1))
psf_ = ops.convert().float32(psf_)
maxPSF = ops.stats().max(psf_).getRealFloat()
psfBackground = psfBackgroundPercent * maxPSF
# subtract background from psf
for t in psf_:
val = t.getRealFloat() - psfBackground
if val < 0:
val = 0
t.setReal(val)
# normalize psf
sumpsf = ops.stats().sum(psf_)
sumpsf = FloatType(sumpsf.getRealFloat())
print sumpsf
psf_ = ops.math().divide(psf_, sumpsf)
# convert image to 32 bit
img_ = ops.convert().float32(data.getImgPlus())
# now deconvolve
deconvolved_ = ops.deconvolve().richardsonLucy(img_, psf_, None, None, None,
None, None, 30, nonCirculant,
acceleration)
def multiviewDeconvolution(images, blockSizes, PSF_kernels, n_iterations, lambda_val=0.0006, weights=None,
filterBlocksForContent=False, PSF_type=PSFTYPE.INDEPENDENT, exe=None, printFn=syncPrint):
"""
Apply Bayesian-based multi-view deconvolution to the list of images,
returning the deconvolved image. Uses Stephan Preibisch's library,
currently available with the BigStitcher Fiji update site.
images: a list of images, registered and all with the same dimensions.
blockSizes: how to chop up the volume of each image for parallel processing.
When None, a single block with the image dimensions is used,
plus half of the transformed kernel dimensions for that view.
PSF_kernels: the images containing the point spread function for each input image. Requirement: the dimensions must be an odd number.
n_iterations: the number of iterations for the deconvolution. A number between 10 and 50 is desirable. The more iterations, the higher the computational cost.
lambda_val: default is 0.0006 as recommended by Preibisch.
weights: a list of FloatType images with the weight for every pixel. If None, then all pixels get a value of 1.
filterBlocksForContent: whether to check before processing a block if the block has any data in it. Default is False.
PSF_type: defaults to PSFTYPE.INDEPENDENT.
exe: a thread pool for concurrent execution. If None, a new one is created, using as many threads as CPUs are available.
printFn: the function to use for printing error messages. Defaults to syncPrint (thread-safe access to the built-in `print` function).
Returns an imglib2 ArrayImg, or None if something went wrong.
"""
mvd_exe = exe
if not exe:
mvd_exe = newFixedThreadPool() # as many threads as CPUs
try:
mvd_weights = weights
if not weights:
mvd_weights = repeat(Views.interval(ConstantRandomAccessible(FloatType(1), images[0].numDimensions()), FinalInterval(images[0])))
for i, PSF_kernel in enumerate(PSF_kernels):
for d in xrange(PSF_kernel.numDimensions()):
if 0 == PSF_kernel.dimension(d) % 2:
printFn("for image at index %i, PSF kernel dimension %i is not odd." % (i, d))
return None
if not blockSizes:
# Whole image dimensions + half of the transformed PSF kernel dimensions
kernel_max = int(max(PSF_kernel.dimension(d)
for d in xrange(PSF_kernel.numDimensions())
for PSF_kernel in PSF_kernels) * 2)
syncPrint("kernel max dimension *2: %i" % kernel_max)
blockSizes = []
for image in images:
blockSizes.append([image.dimension(d) + kernel_max
for d in xrange(image.numDimensions())])
syncPrint("blockSize:" + str(blockSizes[-1]))
cptf = createFactory(mvd_exe, lambda_val, blockSizes[0]) # TODO which blockSize to give here?
filterBlocksForContent = False # Run once with True, none were removed
dviews = [DeconView(mvd_exe, img, weight, PSF_kernel, PSF_type, blockSize, 1, filterBlocksForContent)
for img, blockSize, weight, PSF_kernel in izip(images, blockSizes, mvd_weights, PSF_kernels)]
decon = MultiViewDeconvolutionSeq(DeconViews(dviews, mvd_exe), n_iterations, PsiInitBlurredFusedFactory(), cptf, ArrayImgFactory(FloatType()))
if not decon.initWasSuccessful():
printFn("Something went wrong initializing MultiViewDeconvolution")
return None
else:
decon.runIterations()
return decon.getPSI()
finally:
# Only shut down the thread pool if it was created here
if not exe:
mvd_exe.shutdownNow()
r1 = Rectangle(1708, 680, 1792, 1760)
r2 = Rectangle(520, 248, 1660, 1652)
cut1 = Views.zeroMin(
Views.interval(red, [r1.x, r1.y],
[r1.x + r1.width - 1, r1.y + r1.height - 1]))
cut2 = Views.zeroMin(
Views.interval(red, [r2.x, r2.y],
[r2.x + r2.width - 1, r2.y + r2.height - 1]))
# Thread pool
exe = Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors())
try:
# PCM: phase correlation matrix
pcm = PhaseCorrelation2.calculatePCM(cut1, cut2,
ArrayImgFactory(FloatType()),
FloatType(),
ArrayImgFactory(ComplexFloatType()),
ComplexFloatType(), exe)
# Number of phase correlation peaks to check with cross-correlation
nHighestPeaks = 10
# Minimum image overlap to consider, in pixels
minOverlap = cut1.dimension(0) / 10
# Returns an instance of PhaseCorrelationPeak2
peak = PhaseCorrelation2.getShift(pcm, cut1, cut2, nHighestPeaks,
minOverlap, True, True, exe)
print "Translation:", peak.getSubpixelShift()
print corners3VC
print corners3VR
block3VL = block(imgE, corners3VC)
block3VC = block(imgE, corners3VL)
block3VR = block(imgE, corners3VR)
op5 = sub(block3VC, block3VL, block3VR) # center minus sides
op6 = sub(add(block3VL, block3VR), block3VC) # sides minus center
# Two bright-black-bright horizontal features 4x8 / 4x8 / 4x8
corners3HT = [[x, y - 2] for x, y in cornersHT]
corners3HC = [[x, y + 2] for x, y in cornersHT]
corners3HB = [[x, y + 2] for x, y in cornersHB]
print corners3HT
print corners3HC
print corners3HB
block3HT = block(imgE, corners3HT)
block3HC = block(imgE, corners3HC)
block3HB = block(imgE, corners3HB)
op7 = sub(block3HC, block3HT, block3HB) # center minus top and bottom
op8 = sub(add(block3HT, block3HB), block3HC) # top and bottom minus center
for name, op in ((name, eval(name)) for name in vars()
if re.match(r"^op\d+$", name)):
# For development:
if WindowManager.getImage(name):
continue # don't open
#
opimp = IL.wrap(op.view(FloatType()), name)
opimp.getProcessor().resetMinAndMax()
opimp.show()
def convert(self, sampler):
return FloatType(UnsignedByteToFloatAccess(sampler))
def createFactoryFn(exe, lambda_val, blockSize):
if use_cuda and cuda:
return ComputeBlockSeqThreadCUDAFactory(exe, MultiViewDeconvolution.minValue, lambda_val, blockSize, cuda, HashMap(idToCudaDevice))
else:
return ComputeBlockSeqThreadCPUFactory(exe, MultiViewDeconvolution.minValue, lambda_val, blockSize, ArrayImgFactory(FloatType()))
